Document and Evaluate LLM Prompting Success is an intermediate course for ML engineers and AI practitioners responsible for the stability and performance of live LLM systems. Moving an LLM from a cool prototype to a reliable production service requires more than just clever prompting—it demands operational discipline. This course provides the framework for that discipline.
Document and Evaluate LLM Prompting Success
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Document and Evaluate LLM Prompting Success
This course is part of LLM Optimization & Evaluation Specialization
Instructor: LearningMate
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Intermediate level
Recommended experience
1 hour to complete
Flexible schedule
Learn at your own pace
What you'll learn
Create operational run-books for LLM systems and evaluate prompt patterns to improve performance and reduce operational costs.
Skills you'll gain
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December 2025
Taught in English
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This course is part of the LLM Optimization & Evaluation Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
In this foundational module, learners will explore the critical importance of clear and actionable documentation in the management of production AI systems. They will delve into the reasons why robust documentation is essential, transitioning from a conceptual understanding to the practical creation of a professional-grade run-book. Through a blend of instructional videos, targeted readings, and engaging dialogues, learners will identify key components of effective documentation, adhere to best practices in technical writing, and apply these insights to a realistic scenario: managing a vector index update for a large language model (LLM) system. By the end of the module, participants will be equipped to construct a comprehensive run-book that enhances operational clarity and facilitates effective collaboration among both technical and non-technical stakeholders.
What's included
1 video1 reading2 assignments
This module transitions from system stability to performance optimization by focusing on prompt engineering as a systematic discipline. Learners will discover why ad-hoc prompting fails in production and will learn a structured framework for comparing patterns like Zero-Shot and Few-Shot. They will analyze trade-offs between quality, cost, and consistency, and practice communicating their findings in a format suitable for a team-wide "lunch-and-learn," addressing the second and third learning objectives.
What's included
2 videos2 readings1 assignment
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